1. Field of the Invention
This invention generally relates to methods and systems for classifying defects on a specimen with an adaptive automatic defect classifier.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers. Inspection processes have always been an important part of fabricating semiconductor devices such as integrated circuits. However, as the dimensions of semiconductor devices decrease, inspection processes become even more important to the successful manufacture of acceptable semiconductor devices. For instance, as the dimensions of semiconductor devices decrease, detection of defects of decreasing size has become necessary since even relatively small defects may cause unwanted aberrations in the semiconductor devices.
Once defects have been detected by inspection, additional information for the defects may be generated in one or more manners. For example, the defects may be re-visited by defect review in which a system having resolution capability greater than that used during inspection is used to generate images of the defects. Information about the defects generated using such images may then be used to determine a type (or classification) of the defects. For example, the defects may be classified as particle type defects, bridging type defects, scratch type defects, and the like. Although defect classifications may be determined based on information generated by defect review, sometimes, defect classification is performed based on information generated by inspection (e.g., if the information for the defect generated by inspection is adequate for defect classification and/or for preliminary classification based on the limited amount of information generated by inspection).
The methods, algorithms, and/or systems that perform classification of defects are often referred to as “defect classifiers.” Defect classifier creation and monitoring typically includes three phases: a training phase, a validation phase, and a production phase. In the training phase, data may be collected until M lot results have been collected. An operator may then classify all the defects manually. Once M lot results have been collected, the classifier is created for classes that have more than N defects, where N is a pre-defined value. In the validation phase, data for M lots may be collected, and an operator classifies all the defects manually. If the accuracy of the validation lots is equal to or less than the training lots, the training classifier may be used for production. Otherwise, the validation classifier may be used for production. In the production phase, the contribution of the classifier may be monitored. An operator may classify the non-contribution bin (e.g., low confidence defects). If the confidence drops below a predefined threshold, the training phase may be performed again.
There are, however, a number of disadvantages to the currently performed methods for defect classifier creation and monitoring. For example, the classifier creation and monitoring process is cumbersome and cannot provide a relatively fast response to the dynamic defect changes in the fab. In addition, the user has to wait at least 2×M lots before the first classifier is created. Furthermore, during the training and validation phases, all the defects need to be manually classified and no assisted manual classification is provided. Moreover, if there is a defect shift or excursion, the user needs to wait at least M lots for the new classifier to be released to production. In addition, the training set may be severely imbalanced and not good enough to create a robust classifier. In many cases, the training set includes 90% nuisance and only 10% of the training set includes defects of interest (DOIs). Therefore, the number of defects is not sufficient to create a robust classifier. The currently used methods and systems also do not have a method to decide the robustness of the classifier.
Accordingly, it would be advantageous to develop systems and/or methods for classifying defects on a specimen with an adaptive automatic defect classifier that do not have one or more of the disadvantages described above.